Dear Tommaso, thank you for your kind reply. I know I have a lot to study before actually starting any code and that's why any suggestion is so valuable. So, you're suggesting that a simplification of the system using only the paramagnetic centers can be a good approach? (I'm not sure if I understood it correctly). My main idea was, at first, try to represent the systems as realistically as possible (using coordinates). I know that the software will not know what a bond is or what an intermolecular interaction is but, let's say, after including 1000s of examples in the training, I was expecting that (as an example) finding a C 0.000 and an H at 1.000 should start to "make sense" because it leads to an experimental trend. And I totally agree that my way to represent the system is not the better.
Thank you so much for all the help. On Mon, Mar 27, 2017 at 4:15 PM, Tommaso Costanzo < tommaso.costanz...@gmail.com> wrote: > Dear Henrique, > > > I agree with Robert on the use of a supervised algorithm and I would also > suggest you to try a semisupervised one if you have trouble in labeling > your data. > > > Moreover, as a chemist I think that the input you are thinking to use is > not the in the best form for machine learning because you are trying to > predict coupling J(AB) but in the future space you have only coordinates > (XYZ). What I suggest is to generate the pair of atoms externally and then > use a matrix of the form (Mx3), where M are the pairs of atoms you want to > predict your J and 3 are the features of the two atoms (distance, angle, > unpaired electrons). For a supervised approach you will need a training set > where the J is know so your training data will be of the form Mx4 and the > fourth feature will be the J you know. > > Hope that this is clear, if not I will be happy to help more > > > Sincerely > > Tommaso > > 2017-03-27 13:46 GMT-04:00 Henrique C. S. Junior <henrique...@gmail.com>: > >> Dear Robert, thank you. Yes, I'd like to talk about some specifics on the >> project. >> Thank you again. >> >> On Mon, Mar 27, 2017 at 2:25 PM, Robert Slater <rdsla...@gmail.com> >> wrote: >> >>> You definitely can use some of the tools in sci-kit learn for supervised >>> machine learning. The real trick will be how well your training system is >>> representative of your future predictions. All of the various regression >>> algorithms would be of some value and you make even consider an ensemble to >>> help generalize. There will be some important questions to answer--what >>> kind of loss function do you want to look at? I assumed regression >>> (continuous response) but it could also classify--paramagnetic, >>> diamagnetic, ferromagnetic, etc... >>> >>> Another task to think about might be dimension reduction. >>> There is no guarantee you will get fantastic results--every problem is >>> unique and much will depend on exactly what you want out of the >>> solution--it may be that we get '10%' accuracy at best--for some systems >>> that is quite good, others it is horrible. >>> >>> If you'd like to talk specifics, feel free to contact me at this email. >>> I have a background in magnetism (PhD in magnetic multilayers--i was >>> physics, but as you are probably aware chemisty and physics blend in this >>> area) and have a fairly good knowledge of sci-kit learn and machine >>> learning. >>> >>> >>> >>> On Mon, Mar 27, 2017 at 10:50 AM, Henrique C. S. Junior < >>> henrique...@gmail.com> wrote: >>> >>>> I'm a chemist with some rudimentary programming skills (getting started >>>> with python) and in the middle of the year I'll be starting a Ph.D. project >>>> that uses computers to describe magnetism in molecular systems. >>>> >>>> Most of the time I get my results after several simulations and >>>> experiments, so, I know that one of the hardest tasks in molecular >>>> magnetism is to predict the nature of magnetic interactions. That's why >>>> I'll try to tackle this problem with Machine Learning (because such >>>> interactions are dependent, basically, of distances, angles and number of >>>> unpaired electrons). The idea is to feed the computer with a large training >>>> set (with number of unpaired electrons, XYZ coordinates of each molecule >>>> and experimental magnetic couplings) and see if it can predict the magnetic >>>> couplings (J(AB)) of new systems: >>>> (see example in the attached image) >>>> >>>> Can Scikit-Learn handle the task, knowing that the matrix used to >>>> represent atomic coordinates will probably have a different number of atoms >>>> (because some molecules have more atoms than others)? Or is this a job >>>> better suited for another software/approach? >>>> >>>> >>>> -- >>>> *Henrique C. S. Junior* >>>> Industrial Chemist - UFRRJ >>>> M. Sc. Inorganic Chemistry - UFRRJ >>>> Data Processing Center - PMP >>>> Visite o Mundo Químico <http://mundoquimico.com.br> >>>> >>>> _______________________________________________ >>>> scikit-learn mailing list >>>> scikit-learn@python.org >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> >>>> >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >> >> >> -- >> *Henrique C. S. Junior* >> Industrial Chemist - UFRRJ >> M. Sc. Inorganic Chemistry - UFRRJ >> Data Processing Center - PMP >> Visite o Mundo Químico <http://mundoquimico.com.br> >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > > -- > Please do NOT send Microsoft Office Attachments: > http://www.gnu.org/philosophy/no-word-attachments.html > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > -- *Henrique C. S. Junior* Industrial Chemist - UFRRJ M. Sc. Inorganic Chemistry - UFRRJ Data Processing Center - PMP Visite o Mundo Químico <http://mundoquimico.com.br>
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